Code Repository for GenDexGrasp: Generalizable Dexterous Grasping
by Puhao Li *, Tengyu Liu *, Yuyang Li, Yiran Geng, Yixin Zhu, Yaodong Yang, Siyuan Huang
Generating dexterous grasping has been a longstanding and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior arts focus on a specific type of robot hand, lacking generalizable capability of handling unseen ones. Second, prior arts oftentimes fail to rapidly generate diverse grasps with a high success rate. To jointly tackle these challenges with a unified solution, we propose GenDexGrasp, a novel hand-agnostic grasping algorithm for generalizable grasping.
GenDexGrasp is trained on our proposed large-scale multi-hand grasping dataset MultiDex synthesized with force closure optimization. By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands. Compared with previous methods, GenDexGrasp achieves a three-way trade-off among success rate, inference speed, and diversity.
We first collect a large-scale synthetic dataset for multiple robotic hands with Differentiable Force Closure(DFC). Then, we train a CMap-CVAE to generate hand-agnostic contact maps for unseen objects. We finally optimize grasping poses for unseen hands using the generated contact maps.
Run the following instruction in a conda environment to build the dependencies
conda install requirements.yamlNote that the pytorch-kinematics dependency is modified, you should install it from the source code as following
cd thirdparty/pytorch_kinematics
pip install -e .After install that, run
pip install urdf-parser-pyto install urdf-parser-py lib to resolve the conflict between pytorch-kinematics and urdf-parser-py.
We train and test on 58 daily objects from YCB and ContactDB dataset, together with 5 robotic hands(EZGripper, Barrett Hand, Robotiq-3F, Allegro and Shadowhand) ranging from two to five fingers.
You can download data.zip from Google Drive or Baidu Netdisk, then extract it to the root as
GenDexGrasp
+-- data
| +-- object
| | +-- contactdb
| | +-- ycb
| +-- urdf
| | ...We synthesis a large-scale MultiDex Grasp Dataset with DFC, you can download MultiDex.zip from Google Drive or Baidu Netdisk, then extract it to the root as
GenDexGrasp
+-- MultiDex
| +-- shadowhand
| +-- allegro
| +-- robotiq_3finger
| +-- barrett
| +-- ezgripper
| +-- robot_object_names.jsonWe construct CMapDataset from Grasp Dataset to train our CMap-CVAE model in our pipeline. You can download dataset.zip from Google Drive or Baidu Netdisk, and extract it to the root.
We create a testing task using IsaacGym simulators to evaluate the stability of our generated grasp pose for objects and robotic hands. You can download env.zip from Google Drive or Baidu Netdisk, and extract it to the root as same as data.zip to build the tasks and assets.
Train CMap-CVAE model through train_cvae.py,
python train_cvae.py
--comment: str, comments for your log directory.
--id: int, identify this experiment.
--batchsize: int, training batch size.
--n_epochs: int, training epochs.
--lr: float, training learning rate.
--lw_recon: float, weight of reconstruction loss.
--lw_kld: float, weight of kl-divergence loss.
--ann_temp: float, rising tempreture of 'lw_kld'.
--ann_per_epochs: int, every 'ann_per_epochs' epochs apply 'ann_temp' on 'lw_kld'.
--disable_shadowhand: store_ture, removing shadowhand grasping data, let shadowhand unseen in training.
--disable_allegro: store_ture, removing allegro grasping data.
--disable_robotiq_3finger: store_ture, removing robotiq-3f grasping data.
--disable_barrett: store_ture, removing barrett grasping data.
--disable_ezgripper: store_ture, removing ezgripper grasping data.
--seed: int, global random seed(default: 42).For instance, you can run
python train_cvae.py --id=0 --disable_shadowhand --comment=example --batchsize=128 --n_epochs=36 --lr=1e-4 --lw_recon=1000 --lw_kld=0.05 --ann_temp=1.5 --ann_per_epochs=2 --batches_per_print=500 --attn_loss_alpha=3.to train a CMap-CVAE, which shadowhand is an out-of-domain robotic hand.
After training CMap-CVAE or preparing the pre-trained weight in ./ckpts/, you can inference the contact map for our-of-domain objects through inf_cvae.py,
python inf_cvae.py
--comment: str, comments for your log directory.
--pre_process: str, process operator for inference cmap.
--s_model: str, pre-trained model name you defined, can reorient in this scripts follwing the existing config.
--num_per_seen_object: int, numbers of inference cmaps per in-domain object.
--num_per_unseen_object: int, numbers of inference cmaps per out-of-domain object.For instance, you can run
python inf_cvae.py --comment=example --s_model=PointNetCVAE_SqrtUnseenShadowhand --pre_process=sharp_lift --num_per_seen_object=4 --num_per_unseen_object=64with the pre-trained weight in ./ckpts/SqrtUnseenShadowhand/weights/.
Constructing inference cmap data for pose generation as
GenDexGrasp
+-- dataset
| +-- CMapInfSet
| | +-- DATASET_NAME
| | | +-- DATASET_ID
| | | | +-- cmap_id.pt
| | | | +-- cmap_ood.pt
| | | | +-- split_train_validate_objects.json, you can generate the grasp pose through run_grasp_gen.py
python run_grasp_gen.py
--comment: str, comments for your log directory.
--robot_name: str, name of robotic hand.
--dataset: str, DATASET_NAME.
--dataset_id: str, DATASET_ID.
--max_iter: int, steps for adam optimization.
--steps_per_iter: NO USE.
--num_particles: int, numbers of initial poses.
--learning_rate: float, adam learning rate.
--init_rand_scale: float, ranging from 0 to 1.
--domain: str, 'ood' refers to out-of-domain objects, 'id' refers to in-domain objects,
--object_id: int, object id in sort of object sets in .json file.
--energy_func: str, 'align_dist' refers to aligned distance energy, 'euclidean_dist' refers to euclidean distance energy.For instance, you can run
python run_grasp_gen.py --comment=example --robot_name=shadowhand --dataset=SqrtFullRobots --dataset_id=SharpClamp_A3 --max_iter=100 --num_particles=32 --learning_rate=5e-3 --init_rand_scale=0.5 --domain=ood --object_id=0 --energy_func=align_distto generate the grasping pose for out-of-domain robotic hand shadowhand and out-of-domain object which is the object_id-th object in split_train_validate_objects.json file.
You should firstly download the Isaac Gym Preview 4 release from the Isaac Gym Web, then follow the installation instructions in the documentation.
Test the stability of grasping pose generated from our method through run_grasp_test.py,
python run_grasp_test.py
--comment: str, comment you set when genrate poses.
--robot_name: str, name of robotic hand.
--dataset: str, log name of your generated grasp pose directory in path `logs_gen`.
--domain: str, 'ood' refers to out-of-domain objects, 'id' refers to in-domain objects,
--base_name: energy_func you set when genrate poses.
--mode: str, mode(default: 'test').For instance you can run
python run_grasp_test.py --robot_name=shadowhand --dataset=SqrtUnseenShadowhand-SharpClamp_A3 --domain=ood --base_name=align_dist --comment=exampleto test the stability of grasps generated from our method, which report the success rate of our grasps.
Visualize the MultiDex grasp data through vis_multidex.py
python vis_multidex.py
--robot_name: str, name of robotic hand.
--object_name: str, name of object.
--num_vis: int, number of visualization., and you can get the name list of robotic hand and object in ./MultiDex/robot_object_names.json.
If you find this work is helpful, please consider citing us as
@article{li2022gendexgrasp,
title={GenDexGrasp: Generalizable Dexterous Grasping},
author={Li, Puhao and Liu, Tengyu and Li, Yuyang and Zhu, Yixin and Yang, Yaodong and Huang, Siyuan},
journal={arXiv preprint arXiv:2210.00722},
year={2022}
}
If you have any questions about this work, feel free to contact Puhao Li at [email protected]